Resources
Free tools, calculators, and guides to help you build and scale your software product
A/B Test Calculators
How to Design and Analyze A/B Tests
Learn how to confidently design and analyze your A/B tests using our calculators. This video walks you through the entire experiment lifecycle—from determining how long your test needs to run and how much data to collect, to analyzing results and determining statistical significance. We'll cover the A/B test simulator and all the calculators available on this page, keeping explanations high-level and practical.
Sample Size and Test Duration
Calculate required sample size per variant and estimate test duration. Includes a comparison table showing how sample size and duration change with different MDE values.
Open CalculatorA/B Test Simulator
Simulate A/B tests with different conversion rates and sample sizes. See how statistical significance evolves over time with p-values, confidence intervals, and detailed analysis.
Open CalculatorHow Much Longer Do I Need to Run?
Estimate how many more days you need to run your experiment to achieve statistical significance, assuming your current conversion rates hold steady.
Open CalculatorP-Value and Confidence Interval Over Time
Visualize how both the p-value and confidence interval of your experiment evolve over time as you accumulate more data. Perfect for understanding whether your test is stabilizing and how confident you can be in the observed lift.
Open CalculatorMinimum Detectable Effect (MDE) Calculator
Determine the smallest effect size you can detect given your sample size, baseline rate, confidence level, and statistical power.
Open CalculatorDuration Estimator
Estimate how many days it will take to complete your experiment based on your available daily traffic, number of variants, and required sample size per variant.
Open CalculatorSample Size Per Variant Calculator
Calculate the required sample size per variant needed to detect a given minimum detectable effect (MDE) with your desired confidence level and statistical power.
Open CalculatorP-Value Calculator (Snapshot)
Quickly check if a result is statistically significant based on final totals. Performs a two-proportion z-test and estimates post-test power to help you understand if your test was underpowered.
Open CalculatorConfidence Interval Calculator (Snapshot)
Calculate a confidence interval for the difference in conversion rates between two groups. Get a range of plausible outcomes, not just a binary yes/no answer.
Open CalculatorInteraction Effects Calculator
Detect interaction effects in concurrent A/B tests. Use chi-square and logistic regression to identify when test variants interfere with each other, potentially masking true effects.
Open CalculatorFeature Flag Simulator
Test and simulate feature flag configurations. Create flags with on/off or percentage rollout rules, evaluate them with user IDs and parameters, and track all evaluations in a downloadable ledger.
Open CalculatorFree Resources
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